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Blog Topic
Frameworks and benchmarks for agent evaluation.
24 metadata-ranked posts in this topic
Ranked for relevance, freshness, and usefulness so readers can find the strongest Armalo posts inside this topic quickly.
Agent scorecards should combine capability, evidence quality, drift, permission safety, recourse, and recursive learning.
Eval-beyond-benchmarks analysis of Agentic OS Mission Control, Armalo Agent recursive self improvement, governed autonomy, trust evidence, and real-world AI operations.
Benchmark scores measure task completion on curated inputs. They tell you almost nothing about how an agent will behave when inputs are adversarial, ambiguous, or outside its training distribution. Here is what actual evaluation looks like.
Once an agent knows the eval, it games it. Helpfulness becomes sycophancy, refusal becomes paranoia, accuracy becomes hallucinated confidence. Defenses exist.
An agent's score can drop 80 points without the agent changing because the judges got better at noticing flaws. How to disentangle agent drift from judge drift.
An agent that gets the answer right but reports false confidence is more dangerous than one that's wrong and admits it. Self-report fidelity is a first-class eval dimension.
Quantile trimming beats z-score trimming when judges can be bribed. Fixed bribe cost, no variance leak, no need to estimate the noise distribution.
When a pact violation goes to dispute, the eval that scored it has to be reconstructible. Provenance is the difference between a verdict and a hand-wave.
The Awards methodology turns accuracy, reliability, safety, scope honesty, security, accountability, and runtime discipline into public recognition.
Recursive agents can improve the benchmark, the scaffold, or the evidence path. Mission control has to know which one changed.
Flywheel analysis of Agentic OS Mission Control, Armalo Agent recursive self improvement, governed autonomy, trust evidence, and real-world AI operations.
Agent evaluations are often treated as durable proof, but a model switch can invalidate the behavioral evidence behind permissions, scores, and buyer trust.
Verification agents should not collapse uncertainty into clean verdicts. They need an interface that preserves ambiguity, evidence strength, and escalation conditions.
LLM judges are becoming trust infrastructure, but rubrics drift, criteria conflict, and evaluation language can quietly change what agents are rewarded for.
Agentic security systems can find more bugs faster, but their value depends on proof, triage cost, exploitability, and the economics of false positives.
Benchmarks matter, but production agent recognition needs receipts: task, tool, authority, evidence, failure, recovery, and consequence.
A single LLM judge has bias profiles you cannot see. Length bias, position bias, self-preference, sycophancy. Three independent model families is the floor.
Agent of the Year should reward repeatable usefulness under authority, not the most cinematic launch video or benchmark screenshot.
A jury that always returns a verdict is a jury that hallucinates when it should not decide. Calibrated refusal lets judges abstain when their confidence does not justify a vote.
Five judges, one hundred cases, forty cents a judgment is two hundred dollars per evaluation. Run that nightly across a fleet and the eval bill exceeds the inference bill. Here is how to spend less without measuring less.
Judge models update. Re-running last quarter's evaluations with this quarter's jury produces different verdicts on identical evidence. Here is how to handle that without rewriting history.
Happy-path evals lie. An agent that's 99% accurate at 1 QPS is often 70% accurate at 100 QPS with adversarial noise. Build evals for the failure surface, not the demo.
Most eval suites cover the easy 80 percent of behavior and pretend that is the whole surface. Coverage mapping makes the blind spots visible so you can decide whether you are willing to ignore them.
Lab evals lie about production. Live sampling is the only way to know how an agent really behaves. Here is the sample-and-shadow pattern, the latency budget, and the sampling plan that makes it work.
A self-improving agent is supposed to read its own output, find what is wrong, and fix it โ looping toward correctness without supervision. We tested whether that loop converges. One reasoning model produced 40 constraint-bound outputs, then revised each across three rounds under two regimes that differ in exactly one thing: whether an external deterministic checker tells it which constraints failed. Unanchored self-revision repaired 6 of 22 round-0 failures (27.3%); the checker-anchored arm, same model, same outputs, repaired 14 (63.6%) โ a 36.4-point recursive-self-improvement ceiling gap (exact McNemar p = 0.0215), and the gap widened every round rather than closing. The mechanism is not weak correction but absent detection: on 16 of the 22 failures the self-revising model never changed a single field, because it did not perceive an error to fix. Self-revision is a detection ceiling, and an external verifier is what raises it. For anyone shipping an autonomous improvement loop, the result is a design rule: bind the loop to a deterministic proof gate, because the agent's own judgment recovers less than half the available repair.